Data optimization and analysis

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dc.authorscopusidMohammadreza Shahriari / 56809020200
dc.authorscopusidFarhad Hosseinzadeh Lotfi / 59136674500
dc.authorwosidMohammadreza Shahriari / HKG-8762-2023
dc.authorwosidFarhad Hosseinzadeh Lotfi / J-4615-2019
dc.contributor.authorShahriari, Mohammadreza
dc.contributor.authorHosseinzadeh Lotfi, Farhad
dc.contributor.authorRahmaniperchkolaei, Bijan
dc.contributor.authorTaeeb, Zohreh
dc.contributor.authorSaati, Saber
dc.date.accessioned2025-04-17T12:07:30Z
dc.date.available2025-04-17T12:07:30Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Matematik Bölümü
dc.description.abstractEfficient decision-making within any organization is not just a possibility, but a reality, thanks to the practicality of meticulous data analysis. This chapter delves deeply into an array of data analysis methods that prove to be invaluable in this pursuit. The central focus is directed toward the data envelopment analysis (DEA) technique. This potent tool, which serves as a cornerstone in evaluating the performance of a cluster of analogous decision-making units (DMUs), is not just a theoretical concept, but a practical solution. Throughout the chapter's course, we delve into a diverse range of models that encompass efficiency assessment, benchmarking, ranking, and advancement. Additionally, regression analysis is explored for each DMU. These models inherently accommodate multiple inputs and outputs, thereby facilitating a comprehensive evaluation. It becomes distinctly apparent that intricate DMUs or those governed by specific indicator conditions necessitate the employment of sophisticated models, as classical paradigms might fall short in such intricate scenarios. Furthermore, the chapter casts a spotlight on the support vector machine (SVM) method. SVM, a versatile approach for the classification of data points into discrete sets, is not just a single-use tool, but a versatile solution. It produces a set of rules that enable precise predictions regarding the categorization of a new data point within one of these predefined sets. By harnessing the power of SVM, organizations are not just limited to one type of data analysis, but can proficiently classify incoming data and derive informed decisions rooted in these discerning categorizations. This chapter provides readers with a profound understanding of the methodologies that underlie DEA and SVMs. These instrumental tools empower organizations to extract profound insights from their data reservoirs, thereby equipping them to navigate intricate decision terrains with unwavering assurance. © 2024 Elsevier Inc. All rights reserved.
dc.identifier.citationShahriari, M., Lotfi, F. H., Rahmaniperchkolaei, B., Taeeb, Z., & Saati, S. (2024). Data optimization and analysis. In Decision-Making Models (pp. 209-236). Academic Press.
dc.identifier.doi10.1016/B978-0-443-16147-6.00028-1
dc.identifier.endpage236
dc.identifier.isbn978-044316147-6, 978-044316148-3
dc.identifier.scopus2-s2.0-85202886278
dc.identifier.scopusqualityN/A
dc.identifier.startpage209
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6242
dc.indekslendigikaynakScopus
dc.institutionauthorShahriari, Mohammadreza
dc.institutionauthorHosseinzadeh Lotfi, Farhad
dc.institutionauthoridMohammadreza Shahriari / 0000-0002-7519-3172
dc.institutionauthoridFarhad Hosseinzadeh Lotfi / 0000-0001-5022-553X
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofDecision-Making Models: A Perspective of Fuzzy Logic and Machine Learning
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBenchmarking
dc.subjectData envelopment Analysis
dc.subjectEfficiency
dc.subjectRanking and Progress and Regression
dc.subjectSupport vector Mach
dc.titleData optimization and analysis
dc.typeBook Chapter

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